Smart metering offers an opportunity to collect and store information (such as power consumption) from a utility grid at household level with increased granularity. Although current policy regulations are restrictive from the point of view of the collected data reuse, the storage of this data opens up a possibility for its misuse. If the collected and stored data become available to parties other than the intended user (in this case a utility company), such as law enforcement agencies, marketing agencies and malicious individuals, this could represent a privacy and/or security risk for consumers.
The term “smart grid” is a recently coined term which represents a large number of different technologies aiming at improving existing electrical power distribution networks. Existing power distribution networks tend to be of an aging character, and one of the general goals of smart grid technology is to bring intelligence into networks to improve efficiency and robustness such that they will be more capable of responding to new higher consumption demands.
One way to adjust to new demands is to employ communication and control networks which will enable a frequent scanning of the power network state and carrying out appropriate actions to provide its stability and functionality.
Power data is being collected with increased granularity. Storage of this detailed data in the smart grid introduces concerns about consumers' privacy. These concerns may be justified by the use of non-intrusive appliance load monitors (NALM), which analyse power signals to track appliance usage patterns. Research suggests that information gathered from the power signals accompanied with other available information can be used to build profiles of house occupants. This could represent a serious privacy threat both for individuals, and for companies and government organisations.
One way of addressing privacy requirements is to develop regulatory data privacy frameworks and policies, based on standard privacy principles such as notice, choice and consent. Anonymity services can also help protect privacy. For example, metering data can be aggregated and encrypted. Alternatively, the data can be separated into low frequency attributable data (for example, data used for billing) and high frequency anonymous technical data (for example, data used for demand-side management).